Predicting alternative communication based on textual analysis

ABSTRACT

A processor-implemented method for predicting alternative communications based on textual analysis. The method includes building, by machine learning, a model to predict an optimal communication method, whereby the building includes training the model on a knowledge corpus of historic data and user data, and results of previous predictions in similar circumstances. The method further includes intercepting textual communication within communication channels, wherein the intercepting comprises a keyboard capture, a screen capture, or both a keyboard capture and a screen capture. The method further includes identifying, by pattern analysis, sentiment analysis, and textual analysis, topics, sentiments, and participants within the intercepted textual communication. The method further includes predicting, by the model, the optimal communication method, whereby the optimal communication method comprises continuing the textual communication, a video conference or a telephone conference.

BACKGROUND

The present invention relates, generally, to the field of computing andmore specifically the field of predicting alternative communications.

Textual communication channels like email, short message service (SMS),instant message (IM), and other real time communication systems arecommonly used in work environments because of their immediacy. In manysituations, text (only) communication can take longer to resolve, asparticipating members are inputting their thoughts at different times.These thoughts might create another point of discussion, which mightcreate a new thread, ultimately elongating the time it takes to resolvethe issue. Alternative channels of communications such as videoconferencing and telephone conferencing allow for quicker issueresolution. Allowing for alternative or optimal communication methodswould lead to quicker conflict resolution.

SUMMARY

According to one embodiment, a method, computer system, and computerprogram product for predicting alternative communications based ontextual analysis is provided. The present invention may include aprocessor-implemented method for predicting alternative communicationsbased on textual analysis. The method comprises building, by machinelearning, a model to predict an optimal communication method, wherebythe building includes training the model on a knowledge corpus ofhistoric data and user data, and results of previous predictions insimilar circumstances. Further provided is intercepting textualcommunication within communication channels, wherein the interceptingcomprises a keyboard capture, a screen capture, or both a keyboardcapture and a screen capture. Topics, sentiments, and participants areidentified within the intercepted textual communication, using patternanalysis, sentiment analysis, and textual analysis. The method alsoprovides for predicting, by the model, the optimal communication method.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates an exemplary networked computer environment accordingto at least one embodiment;

FIG. 2 illustrates a process flowchart for predicting alternativecommunication based on textual analysis according to at least oneembodiment;

FIG. 3 illustrates a continued process flowchart for predictingalternative communication based on textual analysis according to atleast one embodiment;

FIG. 4 illustrates an overlay display generated for predictingalternative communication based on textual analysis according to atleast one embodiment;

FIG. 5 illustrates a block diagram of components of a computing deviceof the system for predicting alternative communication based on textualanalysis of FIG. 1 according to at least one embodiment;

FIG. 6 depicts a cloud computing environment according to an embodimentof the present invention;

FIG. 7 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. In the description, details ofwell-known features and techniques may be omitted to avoid unnecessarilyobscuring the presented embodiments.

Embodiments of the present invention relate to the field of computingand more specifically the field of predicting alternativecommunications. The following described exemplary embodiments provide asystem, method, and program product to, among other things, utilizemachine learning such as textual analysis and sentiment analysis topredict whether a phone or video conference would enable users to arriveat issue resolution quicker and save more time than textualcommunication. Therefore, the present embodiment has the capacity toimprove the technical field of computing and more specifically the fieldof predicting alternative communications by analyzing textualcommunication and predicting whether an alternative means would savetime.

As previously described, textual communication channels like email,short message service (SMS), instant message (IM), and other real timecommunication systems are commonly used in work environments. Thesetextual communication channels allow interactive text communicationbetween users, especially in situations where people are working fromhome and/or cannot have the advantage of in-person collaboration. Inmany situations, issues discussed in text only communication can takelonger to resolve, as participating members are inputting their thoughtsat different times. These siloed thoughts can lead to another point ofdiscussion, which can create a new issue in another thread—ultimatelyelongating the time it takes to resolve the original issue.Additionally, many companies utilize forms of person-to-personcommunication, such as telephone, telecommunication and videoconferencing systems, to allow employees to engage in instantinterpersonal discussion. Person-to-person communication typicallyresults in quicker issue resolution compared to long form textualcommunication. Alternative channels of communications such as videoconferencing and telephone conferencing allow for quicker issueresolution. As such, it may be advantageous to, among other things,implement machine learning such as a textual analysis and sentimentanalysis that predicts and notifies users that time can be saved byswitching to a phone call or video conference rather than using textualcommunication.

According to one embodiment, the predictive system utilizes machinelearning to create a model that can be used to predict whichcommunication paths would result in more timely issue resolution. Typesof machine learning utilized to identify factors in the communicationinclude textual analysis, sentiment analysis, and historic patternanalysis. These factors include the topic, knowledge level ofparticipants, user sentiment, length of time to resolve past similarissues, and whether or not the resolution may be considered successful.The output of the analysis is a model that can be used to predictwhether continued textual communications, telephone/video conferencing,or some combination, would result in quicker issue resolution. Based onthe identified topic the model predicts whether either continued textualcommunication, telephone communication/video conferencing or both wouldresult in quicker issue resolution. The system then generates and sendsan overlay to the conversation participants allowing them the option tocontinue textual communication and/or schedule a phone call or videoconference. The overlay may display an estimated amount of time saved byswitching to an alternate communication method. If the participantschoose to switch to phone or video conference, the predictive system mayconnect to the participants' calendars, for example through an API inthe calendar system, and schedule the meeting.

According to one embodiment, the predictive system utilizes textualanalysis and sentiment analysis to identify and evaluate textualidentifiers in the textual communications. Textual identifiers mayconsist of, but are not limited to, screen capture and keyboard capture.Screen capture being a program that allows for identifying text thatappears on a user's monitor while keyboard capture is a program that canbe used for tracking what a user types into a keyboard. Both methodsallow the predictive system to track textual communications withinmultiple textual applications like email, SMS, 1M, or other similar realtime messaging. The output of the textual identifier methods providesinput to the textual analysis and the sentiment analysis for each of theparticipants.

The predictive system uses textual analysis. Textual analysis mayconsist of, but is not limited to, forms of natural language processingalong with information retrieval techniques such as a bag of words modelor word superiority effect model. The textual analysis identifiesfactors such as the topic, user knowledge, user sentiment, userknowledge level, time related to past interactions and outcomes of pastinteractions. The bag of words model can produce a representation thatturns arbitrary text into fixed-length vectors by counting how manytimes each word appears. The bag of words model can be advantageousbecause it can allow for document classification where the occurrence ofeach word is used. The word superiority effect model allows foridentifying letters within words as compared to isolated letters. Theword superiority can distinguish letter combinations whereas the bag ofword model focuses on words independently. The textual analysis tracksand documents the interactions noting text related to the aforementionedfactors for later topical analysis. The textual analysis will gage whenconversations taper off topic as the topic analysis will indicate achange.

The predictive system utilizes sentiment analysis to identify specificemotion levels of the conversation participants. The sentiment analysiscan be used to construct an enhanced perspective of user experiences,and is typically used in combination with other natural languageprocessing features. Sentiment is the classification of emotionsextracted from a piece of text, speech, or document. Sentiment analysisclassifiers divide the spectrum of emotions into positive, negative, andneutral, for example. Sentiment analysis may detect situations ofurgency or user discouragement that may factor into a decision in favorof a timelier path for issue resolution such as video or telephoneconferencing.

The predictive system utilizes a data gathering module that gathers thediscussion thread of any textual interaction in the different textualcommunication channels. The discussion thread will contextually beanalyzed with any existing method of deriving context of topic fromtextual content. The data gathering module will trigger the remainingsteps of the invention.

The predictive system stores historic data in a knowledge corpus.Initially, historic data is loaded into the knowledge corpus from staticdata that exists in enterprise repositories. As the predictive systemexecutes over time, historic data can be updated to include data thatthe data the gathering module records, user information, and userpreferential data. User information includes data existing inenterprises repositories. This includes email archives, support records,issue tickets, the employee online directory, and calendar information.For instance, the predictive system may weight the knowledge level of auser in a given textual exchange higher or lower based on his area ofexpertise as stated in the enterprise repository. User preferential dataincludes data that can be inputted by the user such as preferred pathsof communication and threshold parameters. For instance, a user mayindicate that textual communication is the default communication method.As another example, a user may preference that if textual communicationwould take more than five minutes to resolve the issue, then prefer anin person meeting, including phone or video conference.

The data stored in the knowledge corpus may be stored in data clusters,which tend to produce quicker data retrieval and analysis results,particularly as the predictive system grows through acquisition of moredata. The data may be sorted into clusters by topic, for example, asrelated to human resources, finance, or technical support topics. Dataclusters may be stored on both one or more servers and in a cloudenvironment. Certain branches of data that are accessed more often maybestored to cloud servers for quicker access, while less often accesseddata may be stored to servers. Where to store the data may be done byperforming a frequency analysis to determine the number of times anevent occurs. The predictive system then may create storage bins foreach problem. Most frequently encountered problem/topics can be storedin “object storage” (e.g., server memory) or in the fastest and mostaccessible storage media, and topics of lesser importance stored inslower storage media, until the least referenced data may be stored inarchive storage. Positive results are fed back and improves confidenceintervals over time and improves cluster data based on frequency ofoccurrence of topics. When data is stored in clusters in the cloud or inremote servers, pointers can be stored in the knowledge corpus to pointto the corresponding cloud computing segment or remote server storingthe topic. Additionally, the data clusters maybe stored according totheir related users. For instance, the data clusters may identify anexternal user of the predictive system by IP address and store knowledgebased on that IP address to a specific branch of cluster data. This typeof storage may be beneficial when an external enterprise frequentlyinteracts with the enterprise, for example, to conduct business.Additionally, the predictive system may be licensed to an outsideenterprise. Data stored related to external users can be anonymized toprotect personal confidential information.

The predictive system utilizes a model of a recurrent neural networkarchitecture. There are several model architectures that can implementembodiments of the invention. However, the recurrent neural networkmodel will be used to discuss the details. A recurrent neural networkmodel algorithm remembers its input due to an internal memory. This canmake it suited for machine learning problems that involve sequentialdata. The data collected in the knowledge corpus can be used to make themodel by either a topical analysis, by breaking down situations based ontopic, or by a neural network machine learning and creating a feedbackloop based on best results. In the feedback loop, system output can usedas input to guide future operation. The results of running thepredictive system are recorded to predict best outcomes of future runs.Utilizing topic identification, sentiment analysis, and multiple factorsrecorded in the knowledge corpus the model will be able to predict whichpath, textual and/or conferenced communication, would result in theleast time for issue resolution. The factors identified by textualanalysis are weighted to assist in the running of the model. Factorweights can be assigned initial conditions and can be updated as themodel is trained and used in prediction. For instance, if a userresolves issues quicker for certain subject matter over a certaincommunication path over time the predictive system will weight thiscommunication higher when making its prediction.

Data collected in the knowledge corpus can be used to train the model ofthe recurrent neural network architecture. The model can be trained bycontinuously fine-tuning weights by a back-propagation technique basedon error rates obtained in previous runs.

A portion of the knowledge corpus can be used for training data. Themodel can be initially fit on a training dataset, which is a set ofexamples derived from the knowledge corpus. Training data is static dataconsisting of initial data gathered through repositors. Training datacan be extracted data from various servers/repositories in theenterprise (such as slack and emails). A feedback loop can interactivelytrain the model to a degree of confidence. The feedback loop of themodel utilizes past tracked data gathered by the gathering module andstored in the knowledge corpus as output, and can be input to guidefuture predictions. After reaching a certain user-defined confidencelevel the model enters a validation phase.

When the trained model meets an agreed upon degree of confidence itenters a validation phase. Validation data can be collected throughearly runs of the model in order to undergo data cleansing to ensuredata quality through trial and error. In the validation phase a feedbackloop fine tunes the model by obtaining performance characteristics.Through validation runs the model can configure quality data byobtaining performance characteristics such as accuracy, sensitivity, andspecificity.

A small portion of the knowledge corpus can be used as a testingdataset. The testing dataset can be independent of the training dataset,but follow the same probability distribution as the training dataset.The testing dataset can be used to assess the performance of the model.After the model is trained, tested, and validated to a configured levelof confidence (for instance 75%) it can be made live. The model willcontinue to collect live data and correct itself through the feedback.For instance, data gathering analyzes the initial email sent from usersto request a meeting, the system analyzes for participants, their rolesand knowledge levels, and the context of the content (keywords). Thepredictive system will record the time an interaction takes and learnsthrough the feedback and will predict future similar interactions totake similar time.

For textual communication, the predictive system will generate anoverlay based on the outcome predicted by the model. The overlaydisplays a recommendation of which communication method will result inquicker issue resolution. The model may predict and display to usersthat a calculated amount of time can be saved by switching to a phonecall or video conference, instead of continuing the textualcommunication. All the participants engaged in conversation will receivethe overlay and recommendation and will have the choice to accept orignore the recommendation. If the predictive system predicts analternative other than to continue the textual communication, and usersaccept the recommendation the predictive system will recommend a timefor a conference call based on calendar data of the participants and thecalculated time for completion as a length for the meeting. All usersmust accept the prediction for the conference to be scheduled. Theoverlay includes the predicted time saved by following the predictedbest action. The predictive system will record the outcome of the userand the time allotted to reach that time.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions may be provided to a processor of a computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It will alsobe noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

The following described exemplary embodiments provide a system, method,and program for predicting alternative communication based on textualanalysis that utilizes machine learning model as well as textualanalysis and sentiment analysis to identify the factors such as thetopic, user knowledge, user sentiment, time related to pastinteractions, outcomes of past interactions, and processing theidentified text through data gathered in a knowledge corpus to predictthe most time efficient outcome.

Referring to FIG. 1, an exemplary networked computer environment 100 isdepicted, according to at least one embodiment. The networked computerenvironment 100 may include client computing device 102 and a server 112interconnected via a communication network 114. According to at leastone implementation, the networked computer environment 100 may include aplurality of client computing devices 102 and servers 112, of which onlyone of each is shown for illustrative brevity.

The communication network 114 may include various types of communicationnetworks, such as a wide area network (WAN), local area network (LAN), atelecommunication network, a wireless network, a public switched networkand/or a satellite network. The communication network 114 may includeconnections, such as wire, wireless communication links, or fiber opticcables. It may be appreciated that FIG. 1 provides only an illustrationof one implementation and does not imply any limitations with regard tothe environments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

Client computing device 102 may include a processor 104 and a datastorage device 106A that is enabled to host and run a software program108A such as email or text messaging programs, a data gathering moduleprogram 110A, and a textual analysis program 118A to communicate withthe server 112 via the communication network 114, in accordance with oneembodiment of the invention. Client computing device 102 may be, forexample, a mobile device, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing device capable of running a program and accessing a network.As will be discussed with reference to FIG. 5, the client computingdevice 102 may include internal components 1302 a and externalcomponents 1304 a, respectively.

The server computer 112 may be a laptop computer, netbook computer,personal computer (PC), a desktop computer, or any programmableelectronic device or any network of programmable electronic devicescapable of hosting and running a system for predicting alternativecommunication (system) 120 a data gathering module 110B, textualanalysis program 118B, and a knowledge corpus 116 communicating with theclient computing device 102 via the communication network 114. Inaccordance with embodiments of the invention. The server computer 112may also host and run a data storage device 106B and software program108B. As will be discussed with reference to FIG. 5, the server computer112 may include internal components 1302 b and external components 1304b, respectively. The server 112 may also operate in a cloud computingservice model, such as Software as a Service (SaaS), Platform as aService (PaaS), or Infrastructure as a Service (IaaS). The server 112may also be located in a cloud computing deployment model, such as aprivate cloud, community cloud, public cloud, or hybrid cloud.

According to the present embodiment, the data gathering module program110A, 110B may be a program capable of identifying factors in a textualinteraction and predicting whether alternative paths of communicationsmay result in quicker issue resolution. The system 120 is explained infurther detail below with respect to FIGS. 2-7.

Referring now to FIG. 2, an operational flowchart illustrating a processflow for system 120 is depicted according to at least one embodiment. At202, the system 120 intercepts comments in a textual communicationchannel by means of the gathering module 110A, 110B. Two or moreparticipants maybe engaged in textual communication. Data can becaptured by keyboard or screen capture. Textual communication can becapture prior to the user sending the communication.

At 204, the system 120 identifies the topic of conversation. The system120 utilizes the data gathering module 110 to identify topic and userinformation by means of textual and sentiment analysis. Textual andsentiment analysis identifies the topic and participants in the capturedtext, including topic, user knowledge, and indications of urgency orfrustration, for example.

At 206, the system 120 analyses historical data for topic and user dataand captures average times for resolving similar issues. The textidentified in 202 and analyzed in 204 can be processed by the neuralnetwork machine learning model, building a feedback loop based on bestresults. The identified factors of 204 will be processed through theknowledge corpus to identify matches with branches of clustered data inthe knowledge corpus. The model will use the factors to predict issueresolution time. The data can be organized by weights assigned to thedifferent factors. Factor weights are assigned initial conditions andthen continuously altered using back propagation technique. Forinstance, in a conversation between a server engineer and anotherparticipant, the system 120 can identify, based on the entityrelationship, that the question relates to servers. The system 120 canidentify that the server engineer is familiar with the server issue, sothat his knowledge level can be weighted heavily. For instance, thesystem 120 may recognize that the server engineer is linked to thisissue 90% of the time. The model will use the relationship and weightsto direct the system 120 to look at the branch of historic data relatedto server issues and the server engineer. The data related to this issuewill then be inputted back into the feedback loop to affect futuredecisions.

At 208, the system 120 determines whether to recommend creating ameeting invite. If the model determines from the historic data in theknowledge corpus that telephone or video conferencing or a personalmeeting would lead to quick issue resolution (at 210, “YES” branch), themodel may continue to step 210 where the system 120 predicts that videoconferencing will result in time saved. If the model determines that no,or miniscule, time would be saved by a telephone or video conference andthat textual analysis would be just as effective for issue resolution(at 212, “NO” branch), the data model may continue to 212 where thesystem 120 predicts that textual communication will save time and sendsa continuation notification.

At 210, the system 120 predicts that video conferencing will result inquicker issue resolution. This prediction can be based on the trackeddata and/or training data and preferential data of the user in contextwith its interaction with the topic. For instance, the issue between theserver engineer and the other participant will determine that a video ortelephone call will result in quicker issues resolution based onhistoric data showing such. The system 120 analyzes the data todetermine that time would be saved if the users were to use videoconferencing or telephone communications. The system continues to FIG.3.

At 212, the system 120 predicts that textual communication will resultin quicker issue resolution. The system 120 analyzes the data to make aprediction based on the historic data that was used to train the model.In this situation the model will show that minimum or no time would besaved by the video or telephone conferencing. The system continues toFIG. 3.

Referring now to FIG. 3, a continued process flowchart for predictingalternative communication based on textual analysis 300 according to atleast one embodiment. At 302 the system 120 generates an overlaynotification. The overlay notification will display a recommendation ofhow to proceed that will result in quicker issue resolution based on theprevious steps of the flowchart.

At 303 the system 120 predicts an estimated amount of time that may besaved by changing to an alternative communication method. The system 120utilizes tracked data and/or training data as well as preferential datato determine the estimated time. Tracked data related to similarinstances will allow the system to gage an estimated time for resolutionrelated to specific topics or conversation participants. Time predictedcan be estimated by averaging several past recorded times such as, usertimes to read and send emails and times between conversations on videoconferencing or telephone calls. The system will gage time based oninitiation and end of the communication. For textual communications, thesystem 120 may us a count of a number of texts/emails in thecommunication chain. When no more exchanges are detected over a definedperiod of time, the system 120 assumes that the end is reached.Sentiment analysis may indicate whether or not the communication chainresulted in a positive resolution. Over time, the system 120 will haverecorded data related to time for a temporal analysis that can determinethe fastest paths of resolution for certain issues.

At 304 the system 120 sends the overlay notification to the participantsshowing the predicted time saved. The overlay notification includes thepredicted communication path that would save the most time, and anestimated amount of time the predicted path may save. All participantsreceive the same overlay notification. The overlay will have an optionto approve the prediction or ignore it.

At 306 the system 120 records if the participants accept thenotification and, if so, schedules a meeting according with theparticipants' calendar data. The system 120 will have integrationbetween the textual communication systems and the calendar/meetingsystem, thereby allowing the system 120 to recognize availability of thedifferent participants. All participants must accept the prediction inorder for the conference to be scheduled.

At 308 the system 120 records whether textual resolution was reached, ormeeting was held by video conference or telephone, and the timeconsumed. If video conferencing is predicted, the system 120 can recordif the participants hold a video conference. If the participants chooseto use telephone communications, the system 120 can identify and recordwhether a phone conference had occurred. In situations where videoconferencing or telephone communication are had the system 120 willutilize textual analysis in subsequent communication for textual orsentimental pointers to successful conclusion. Where there is nosubsequent communication the system 120 can assume and record asuccessful conclusion to the issue. The system then continues to 310 andends.

At 310 the system 120 records in the knowledge corpus, the data of allthe previous interactions, including capturing the topic, user knowledgelevel, user sentiment, time related to past interactions and outcomes ofpast interactions of a textual communication. The time of all theinteractions can be recorded to the knowledge corpus for use in thefeedback loop for building and improving the model for preferredoutcomes in future runs. This leverages and trains the system for futureruns of similar interactions.

Referring now to FIG. 4, an overlay display 400 generated for predictingalternative communication is illustrated. The overlay presents theprediction made by the model to the participants. The information box410 can display information regarding the subject of the communication,the date and time, the participants involved and the location link orphone call number of the proposed conference.

The invitation option 415 can display an invitation option. This allowsparticipants to accept the prediction and schedule a conference with theother participants. All other participants must accept the prediction inorder for the system 120 to generate a conference invite. By selectingthe 420 option the participants may choose to ignore the prediction andcontinue with textual communication. In the text box 425, participantsmay display messages related to the issue. An estimated time saved canbe displayed in the message box 430. The proposed time to be saved canbe calculated based on historic data in the knowledge corpus and thepast interactions that have trained the model.

FIG. 5 shows a block diagram 1300 of internal and external components ofthe client computing device 102 and the server 112 depicted in FIG. 1 inaccordance with an embodiment of the present invention. It should beappreciated that FIG. 5 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The data processing system 1302, 1304 can represent any electronicdevice capable of executing machine-readable program instructions. Thedata processing system 1302, 1304 may be representative of a smartphone, a computer system, PDA, or other electronic devices. Examples ofcomputing systems, environments, and/or configurations that mayrepresented by the data processing system 1302, 1304 include, but arenot limited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, network PCs, minicomputersystems, and distributed cloud computing environments that include anyof the above systems or devices.

The client computing device 102 and the server 112 may includerespective sets of internal components 1302 a,b and external components1304 a,b illustrated in FIG. 5. Each of the sets of internal components1302 a,b include one or more processors 1320, one or morecomputer-readable RAMs 1322, and one or more computer-readable ROMs 1324on one or more buses 1326, and one or more operating systems 1328 andone or more computer-readable tangible storage devices 1330. The one ormore operating systems 1328, the software program 108A, 108B and thedata gathering module 110A in the client computing device 102, and thedata gathering module 110B in the server 112 are stored on one or moreof the respective computer-readable tangible storage devices 1330 forexecution by one or more of the respective processors 1320 via one ormore of the respective RAMs 1322 (which typically include each memory).In the embodiment illustrated in FIG. 5, each of the computer-readabletangible storage devices 1330 is a magnetic disk storage device of aninternal hard drive. Alternatively, each of the computer-readabletangible storage devices 1330 is a semiconductor storage device such asROM 1324, EPROM, flash memory or any other computer-readable tangiblestorage device that can store a computer program and digitalinformation.

Each set of internal components 1302 a,b also includes a R/W drive orinterface 1332 to read from and write to one or more portablecomputer-readable tangible storage devices 1338 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the datagathering program 110A, 110B, can be stored on one or more of therespective portable computer-readable tangible storage devices 1338,read via the respective R/W drive or interface 1332, and loaded into therespective hard drive 1330.

Each set of internal components 1302 a,b also includes network adaptersor interfaces 1336 such as a TCP/IP adapter cards, wireless Wi-Fiinterface cards, or 3G or 4G wireless interface cards or other wired orwireless communication links. The software program 108 and the datagathering module 110A in the client computing device 102 and the datagathering module 110B in the server 112 can be downloaded to the clientcomputing device 102 and the server 112 from an external computer via anetwork (for example, the Internet, a local area network or other, widearea network) and respective network adapters or interfaces 1336. Fromthe network adapters or interfaces 1336, the software program 108 andthe data gathering module 110A in the client computing device 102 andthe data gathering module 110B in the server 112 are loaded into therespective hard drive 1330. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 1304 a,b can include a computerdisplay monitor 1344, a keyboard 1342, and a computer mouse 1334.External components 1304 a,b can also include touch screens, virtualkeyboards, touch pads, pointing devices, and other human interfacedevices. Each of the sets of internal components 1302 a,b also includesdevice drivers 1340 to interface to computer display monitor 1344,keyboard 1342, and computer mouse 1334. The device drivers 1340, R/Wdrive or interface 1332, and network adapter or interface 336 comprisehardware and software (stored in storage device 1330 and/or ROM 1324).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 6, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 100 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 100 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 6 are intended to be illustrative only and that computing nodes100 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 7, a set of functional abstraction layers 500provided by cloud computing environment 50 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 7 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and a system for predicting alternativecommunication 96. The system for predicting alternative communication 96may relate to building a model of past communications including textualand non-textual, analyzing a textual communication among participants todetermine the topic of the communication and knowledge role of theparticipants, and predicting by the model whether an alternate form ofcommunication, e.g., conference call, may result in quicker resolutionthan continuing textual communication.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A processor-implemented method for predicting alternative communications based on textual analysis, the method comprising: building, by machine learning, a model to predict an optimal communication method, wherein the building includes training the model on a knowledge corpus of historic data and user data, and results of previous predictions in similar circumstances; intercepting textual communication within communication channels, wherein the intercepting comprises a keyboard capture, a screen capture, or both a keyboard capture and a screen capture; identifying, by pattern analysis, sentiment analysis, and textual analysis, topics, sentiments, and participants within the intercepted textual communication; and predicting, by the model, the optimal communication method.
 2. The method of claim 1, further comprising: generating an overlay notification for all participants, based on the predicted optimal communication method being other than continuing the textual communication; based on the participants accepting the predicted optimal communication method, generating calendar entries for each participant; and based on the participants rejecting the predicted optimal communication method, continuing the textual communication.
 3. The method of claim 1, wherein the knowledge corpus is stored in a cloud computing segment by topic, wherein the topic is determined by topical analysis of the knowledge corpus, and wherein a pointer is stored in the knowledge corpus to point to the corresponding cloud computing segment storing the topic.
 4. The method of claim 1, wherein the knowledge corpus initially includes enterprise repositories, wherein the enterprise repositories comprise email archives, technical problem reports, and employee online directories, and wherein the knowledge corpus is updated through a feedback loop with data describing factors identified in previous interactions between different participants, an amount of time for each interaction, and whether the optimal communication method was accepted or rejected.
 5. The method of claim 4, wherein the factors identified in the previous interactions are selected from a group consisting of topic, area of expertise of the participants, length of time to resolve past similar issues, method of communication, and whether the resolution was successful.
 6. The method of claim 4, wherein the factors are assigned weights that are fine-tuned by a back-propagation technique based on error rates obtained in previous runs of the method.
 7. The method of claim 1, wherein the time saved is determined by averaging past recorded times, wherein the past times are selected from a group consisting of user time to read email, user time to send emails, and total elapsed time between conversations.
 8. A computer system for predicting alternative communications for time saving in issue resolution, the computer system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising: building, by machine learning, a model to predict an optimal communication method, wherein the building includes training the model on a knowledge corpus of historic data and user data, and results of previous predictions in similar circumstances; intercepting textual communication within communication channels, wherein the intercepting comprises a keyboard capture, a screen capture, or both a keyboard capture and a screen capture; identifying, by pattern analysis, sentiment analysis, and textual analysis, topics, sentiments, and participants within the intercepted textual communication; and predicting, by the model, the optimal communication method.
 9. The computer system of claim 8, further comprising: generating an overlay notification for all participants, based on the predicted optimal communication method being other than continuing the textual communication; based on the participants accepting the predicted optimal communication method, generating calendar entries for each participant; and based on the participants rejecting the predicted optimal communication method, continuing the textual communication.
 10. The computer system of claim 8, wherein the knowledge corpus is stored in a cloud computing segment by topic, wherein the topic is determined by topical analysis of the knowledge corpus, and wherein a pointer is stored in the knowledge corpus to point to the corresponding cloud computing segment storing the topic.
 11. The computer system of claim 8, wherein the knowledge corpus initially includes enterprise repositories, wherein the enterprise repositories comprise email archives, technical problem reports, and employee online directories, and wherein the knowledge corpus is updated through a feedback loop with data describing factors identified in previous interactions between different participants, an amount of time for each interaction, and whether the optimal communication method was accepted or rejected.
 12. The computer system of claim 11, wherein the factors identified in the previous interactions include topic, area of expertise of the participants, length of time to resolve past similar issues, method of communication, whether the resolution was successful.
 13. The computer system of claim 11, wherein the factors are assigned weights that are fine-tuned by a back-propagation technique based on error rates obtained in previous runs of the method.
 14. The computer system of claim 8, wherein the time saved can be determined by averaging several past recorded times such as, user times to read and send emails and times between conversations.
 15. A computer program product for predicting alternative communications for time saving in issue resolution, the computer program product comprising: one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor, the program instructions comprising: building, by machine learning, a model to predict an optimal communication method, wherein the building includes training the model on a knowledge corpus of historic data and user data, and results of previous predictions in similar circumstances; intercepting textual communication within communication channels, wherein the intercepting comprises a keyboard capture, a screen capture, or both a keyboard capture and a screen capture; identifying, by pattern analysis, sentiment analysis, and textual analysis, topics, sentiments, and participants within the intercepted textual communication; and predicting, by the model, the optimal communication method.
 16. The computer program product 15, further comprising: generating an overlay notification for all participants, based on the predicted optimal communication method being other than continuing the textual communication; based on the participants accepting the predicted optimal communication method, generating calendar entries for each participant; and based on the participants rejecting the predicted optimal communication method, continuing the textual communication.
 17. The computer program product 15, wherein the knowledge corpus is stored in a cloud computing segment by topic, wherein the topic is determined by topical analysis of the knowledge corpus, and wherein a pointer is stored in the knowledge corpus to point to the corresponding cloud computing segment storing the topic.
 18. The computer program product 15, wherein the knowledge corpus initially includes enterprise repositories, wherein the enterprise repositories comprise email archives, technical problem reports, and employee online directories, and wherein the knowledge corpus is updated through a feedback loop with data describing factors identified in previous interactions between different participants, an amount of time for each interaction, and whether the optimal communication method was accepted or rejected.
 19. The computer program product 18, wherein the factors identified in the previous interactions include topic, area of expertise of the participants, length of time to resolve past similar issues, method of communication, whether the resolution was successful; and the factors are assigned weights that are fine-tuned by a back-propagation technique based on error rates obtained in previous runs of the method.
 20. The computer program product 15, wherein the time saved can be determined by averaging several past recorded times such as, user times to read and send emails and times between conversations. 